围绕红外视频流机动目标识别中存在的图像噪声大、对比度低的问题, 首先通过改进中值滤波算法对图像进行预处理, 在此基础上, 提出通过自适应改变描述背景像素高斯分布个数的方法来提高建模效率的策略, 并将帧差法和背景差分法相结合起来用于提取机动目标。当检测到多个机动目标时, 提出一种计算连通域坐标的方法以完成目标的定位, 并运用Haar_like矩形来完成对目标的特征提取。最后通过支持向量机完成机动目标的识别。大量的实验结果表明, 提出的算法能够高效的实现红外视频中行人和车辆的实时识别。

For the problem of infrared video in maneuvering target recognition such as high noise and low contrast, an improvement approach based on Median filter algorithm is put forward to image preprocessing. On this basis, a method using the description of adaptive change background pixel number of Gauss distribution is presented to reduce background modeling time, and a new algorithm which combines the background difference method with the frame difference method is used to extract the maneuvering targets. When multiple maneuvering targets are detected, an algorithm based on the coordinates of the center of gravity connected domain mark is applied to locate the target area. Then Haar_like rectangular feature is selected to extract target features. Finally, maneuvering target recognition is achieved through supported vector machine (SVM). Experimental results show that the proposed algorithm can efficiently identify people and vehicles in the infrared video in real time.